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The functionality of Moloney murine leukemia virus reverse transcriptase (MMLV RT) will increase with the improvement of its solubility and thermal stability. Introduce directed mutation at specific positions of the MMLV RT sequence and codon optimization is needed to achieve these properties. The two RT coding sequences with (rRT-K) and without directed mutations (rRT-L) were versatility optimized and expressed to analyze the ribonuclease H (RNase H) inactivity and thermostable polymerase activity. For this purpose, the five-point mutations (438–442aa) and three-point mutations (530, 568, and 659 aa) were done at the RT connection domain and RNase H active site, respectively. High expression levels of rRT-L and rRT-K were obtained in E. coli BL21(DE3) and BL21(shuffle) strains, 0.5 mM IPTG concentration at 37 °C, and 8 hours' post-induction condition. Then, recombinant enzymes were purified and verified by Ni-NTA resin and western blotting. Insilico analysis (IUpred 3.0) showed that the directed mutation in the RNase H domain caused the formation of disorder regions or instability in the RNase H domain of rRT-K compared to rRT-L. The modified RT-PCR and the RT-LAMP reactions proved the RNase H inactivity of rRT-K. In addition, increasing of thermostability of rRT-K compared to rRT-L and commercial RT was evaluated by the RT-PCR and RT-LAMP reactions. The results showed that rRT-K could successfully tolerate 60 ºC in the two methods. This study revealed that the directed mutations and the versatile sequence optimization can promise to produce thermostable commercial enzymes to decrease non-specific one-step RT-PCR and RT-LAMP products. Key points: • The versatile sequence optimization to improve the yield of the recombinant enzyme. • Directed specific mutations can promise to produce thermostable and soluble RT commercial enzymes. • Predicted hot mutations in the active site of the RNase H domain of recombinant RT without any effect on its structure. [ABSTRACT FROM AUTHOR]

Abstract The functionality of Moloney murine leukemia virus reverse transcriptase (MMLV RT) will increase with the improvement of its solubility and thermal stability. Introduce directed mutation at specific positions of the MMLV RT sequence and codon optimization is needed to achieve these properties. The two RT coding sequences with (rRT-K) and without directed mutations (rRT-L) were versatility optimized and expressed to analyze the ribonuclease H (RNase H) inactivity and thermostable polymerase activity. For this purpose, the five-point mutations (438–442aa) and three-point mutations (530, 568, and 659 aa) were done at the RT connection domain and RNase H active site, respectively. High expression levels of rRT-L and rRT-K were obtained in E. coli BL21(DE3) and BL21(shuffle) strains, 0.5 mM IPTG concentration at 37 °C, and 8 hours’ post-induction condition. Then, recombinant enzymes were purified and verified by Ni-NTA resin and western blotting. Insilico analysis (IUpred 3.0) showed that the directed mutation in the RNase H domain caused the formation of disorder regions or instability in the RNase H domain of rRT-K compared to rRT-L. The modified RT-PCR and the RT-LAMP reactions proved the RNase H inactivity of rRT-K. In addition, increasing of thermostability of rRT-K compared to rRT-L and commercial RT was evaluated by the RT-PCR and RT-LAMP reactions. The results showed that rRT-K could successfully tolerate 60 ºC in the two methods. This study revealed that the directed mutations and the versatile sequence optimization can promise to produce thermostable commercial enzymes to decrease non-specific one-step RT-PCR and RT-LAMP products.

Aptamers as a kind of biological recognition element have shown great potential in monitoring and the rapid quantification of organophosphorus pesticides (OPPs). However, molecules of OPPs are structurally similar and original aptamers selected by systematic evolution of ligands by exponential enrichment are usually long-chain bases, which hamper the further application under OPPs-aptamer recognition. The aim of the research was to develop a new strategy to design oligonucleotide sequences for binding OPPs by combination of experimental and molecular modeling methods. 3D models of aptamers binding OPPs were constructed, and binding energy and the most probable binding site for the OPPs were then determined by molecular docking, and the binding sites were further confirmed by the results of 2-AP replaced experiments. Based on the docking results, a new aptamer for detection 4 representative OPPs with only 29 bases was designed by reasonable truncation and mutation of the reported aptamer (named S4-29). The interaction between this new aptamer and OPPs were analyzed by molecular docking, microscale thermophoresis, circular dichroism and fluorometric analysis. The results revealed that the new aptamer exhibit more superior recognition performance to OPPs, which can be promote the monitoring ability of OPPs contaminations in food.

Abstract High-frequency mutations in tumor genomes could be exploited as an asset for developing tumor vaccines. In recent years, with the tremendous breakthrough in genomics, intelligence algorithm, and in-depth insight of tumor immunology, it has become possible to rapidly target genomic alterations in tumor cell and rationally select vaccine targets. Among a variety of candidate vaccine platforms, the early application of mRNA was limited by instability low efficiency and excessive immunogenicity until the successful development of mRNA vaccines against SARS-COV-2 broken of technical bottleneck in vaccine preparation, allowing tumor mRNA vaccines to be prepared rapidly in an economical way with good performance of stability and efficiency. In this review, we systematically summarized the classification and characteristics of tumor antigens, the general process and methods for screening neoantigens, the strategies of vaccine preparations and advances in clinical trials, as well as presented the main challenges in the current mRNA tumor vaccine development.

The L1 cell adhesion molecule (L1CAM) plays important roles in the development and plasticity of the nervous system as well as in tumor formation, progression, and metastasis. New ligands are necessary tools for biomedical research and the detection of L1CAM. Here, DNA aptamer yly12 against L1CAM was optimized to have much stronger binding affinity (10–24 fold) at room temperature and 37 °C via sequence mutation and extension. This interaction study revealed that the optimized aptamers (yly20 and yly21) adopted a hairpin structure containing two loops and two stems. The key nucleotides for aptamer binding mainly located in loop I and its adjacent area. Stem I mainly played the role of stabilizing the binding structure. The yly-series aptamers were demonstrated to bind the Ig6 domain of L1CAM. This study reveals a detailed molecular mechanism for the interaction between yly-series aptamers and L1CAM and provides guidance for drug development and detection probe design against L1CAM. [ABSTRACT FROM AUTHOR]

The method used and results found by the team "ASTL-NUAA" from NUAA for the problem of GTOC 11 are presented in the paper. The problem is mainly decomposed into three parts: multiple targets flyby sequence optimization for the Mother Ships, station-building database construction problem of asteroid transfer arcs, and station-building mission planning. Therefore, a three-step optimization framework is proposed to solve the problem. After analyzing the data of all the asteroids and selecting according to the orbital elements and mass of the asteroids, an asteroid candidate set RS1 is chosen as candidate targets for the Mother Ships. A two-level optimization algorithm based on a genetic principle is applied to solve the two-impulse asteroid flyby problem and rapidly determine the asteroid sequence of each Mother Ship. Subsequently, a method of filtering input data for the time-optimal rendezvous problem by the optimal flight time and the corresponding optimal relative phase is proposed to construct a station-building database with less computation burden. The optimal relative phase is obtained by solving free final-phase continuous low-thrust time-optimal problem. The station-building database is composed of the results of continuous low-thrust time-optimal rendezvous problems. At last, a branch-and-bound algorithm based on the greedy strategy is adopted to rapidly get the result by maximizing the minimal mass among the stations. The final score of our solution is 4315.6663, which is obtained after the competition. • The method used and results found by NUAA for the GTOC 11 is presented. • The flyby sequence of asteroids is obtained by the two-level optimization algorithm. • For less computation burden, a filter strategy is proposed to filter the input of time-optimal problem. • To assign asteroids, the branch-and-bound algorithm is applied. [ABSTRACT FROM AUTHOR]

Abstract Messenger RNA (mRNA) vaccines are third‐generation nucleic acid vaccines developed after first‐generation inactivated and live‐attenuated vaccines and second‐generation subunit and viral vector vaccines, characterized by a rapid response to pathogen mutation, simple production process, and high production capacity. The basic mechanism through which mRNA vaccines provide immune protection is the introduction into the body of mRNA expressing a target antigen through a specific delivery system and expression of the corresponding protein in vivo, which stimulates a specific immunological response. Multiple mRNA vaccine platforms against infectious diseases and cancers have shown encouraging results in both animal models and human subjects; in particular, mRNA vaccines against COVID‐19 have been widely adopted around the world. However, the development of mRNA vaccines has not been straightforward. The rapid progress of mRNA vaccines would not have been possible without major recent advances in innate immune sensing and in vivo delivery strategies. Creative research in mRNA design, lipid/polymer/novel nanocarrier development, and coupling to wearable/implantable electrostimulation medical devices may drive the evolution of mRNA vaccines.

In modulated wideband converter (MWC), all signals are aliased and sampled indiscriminately, which reduces the reconstruction accuracy of target signal. The focused compressed sampling uses optimized mixing sequence to increase the mixing gain of the target signal. To establish the sequence library with selective characteristics, this brief analyzes the signal components aliased into baseband, then proposes a sequence optimization method based on the greedy algorithm. Simulation and experiments show that the optimized sequence possesses a high gain for a desired frequency band signal. In the three-carrier signal test, as the approximate desired signal power ratio (DSR) is raised to 0.99 in baseband, the normalized mean square error (NMSE) of the reconstructed desired signal is improved by nearly 10 dB compared to using pseudorandom sequences. [ABSTRACT FROM AUTHOR]

To improve the efficiency of complex assemblies in large-scale assembly sequence planning, an intelligent sequence planning method for constructing an assembly hybrid G-diagram model to realize the hierarchy of assembly structures is proposed. The assembly hybrid G-diagram model is constructed according to the assembly relationship semantics, and the assembly relationship semantics can be transformed into the corresponding assembly connection matrix and assembly priority matrix. Subassembly discriminant conditions are given to realize subassembly and isolated parts extraction, and the assembly structure is divided into part-level and subassembly-level. According to the assembly hybrid G-diagram, all feasible assembly sequences of part-level (within subassembly) and subassembly-level (subassembly as a whole) are solved, respectively. The particle swarm algorithm is used to optimize the assembly sequence with the goal of aggregation and redirection. The optimal sequence of part-level and subassembly-level is obtained, respectively. The sequence information is integrated to obtain the complete assembly sequence with the highest assembly efficiency under parallel planning. The feasibility and effectiveness of the assembly sequence optimization method are verified using a V-type dual-cylinder engine as an example. This planning method can greatly reduce the search space and avoid infeasible sequences when solving assembly sequences. In parallel planning, the sequence optimization process can be greatly shortened to ensure the assembly efficiency. [ABSTRACT FROM AUTHOR]

The turbine rotor is the key component of the turbine, which has a great impact on the construction cost and power generation efficiency of an entire hydropower station. Receiving the torque of the runner transmission and completing the specified power generation is its main function. There are many uncertain factors in the design, manufacture, and operation environment of a turbine rotor. Therefore, it is necessary to optimize the mechanism on the premise of ensuring that the mechanical system meets high reliability and high safety levels. This article uses the multidisciplinary reliability analysis and optimization method under random and interval uncertainty to quantitatively analyze the uncertainty factors, and then optimally solves the RBMDO problem of the turbine rotor mechanism. Through the finite element simulation analysis of the optimized design scheme, the rationality and feasibility of the obtained results are further verified. [ABSTRACT FROM AUTHOR]

The redundancy allocation problem (RAP) is an intriguing area in the field of reliability optimization to which a lot of research has been devoted in recent years. In this paper, a bi-objective model is developed for RAP with a heterogeneous backup scheme and a mixed redundancy strategy. Elimination of the lower bound estimation and the exact calculation of the reliability of the mixed strategy forms one of the most striking features of the proposed model. Investigating the optimal sequence of components in each subsystem, the study modifies a non-dominated sorting genetic algorithm (NSGA-II), as a powerful multi-objective evolutionary one, to solve the proposed bi-objective model. Two numerical examples will then be used to verify the efficiency of the model in achieving enhanced system reliability. Finally, the results of Pareto optimal set are used to demonstrate that the assumptions made enable the proposed model to improve system reliability by offering various structures while simultaneously considering system limitations. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) is an increasingly utilized treatment for multiple neurological disorders. Continued improvements in DBS outcome are, in part, related to increasing ability to directly visualize stimulation targets by MRI. However, it is challenging to image DBS targets with conventional MRI techniques due to limited contrast. Fast Gray Matter Acquisition T1 Inversion Recovery (FGATIR) is a commonly used MRI sequence that improves visualization of several key DBS targets by suppressing white matter (WM) signal to better reveal deep-brain gray matter (GM) structures. Due to increased signal level at high field strength, application of FGATIR on 7T MRI may allow higher spatial resolution and better DBS targeting accuracy. However, successful utilization of FGATIR requires meticulous sequence optimization involving multiple parameters to maximize GM signal while suppressing WM. This is further complicated by the transmit RF field (B1+) inhomogeneity on 7T, which can cause severe contrast degradation. In this work, we introduce a systematic approach to optimize FGATIR and to improve visualization of thalamic DBS targets on 7T. FGATIR optimization is cast into a constrained optimization problem whose objective function and constraints are designed to maximize the GM-WM contrast-to-noise ratio (CNR) while accounting for B1+ inhomogeneity. This approach allows a systematic search for optimal parameters across the multi-dimensional parametric space while limiting the negative effect of B1+ variation. Bloch equation simulations were performed to solve the proposed optimization problem and to compare the sequence derived from this method against the sequence optimized without considering B1+ inhomogeneity. The results showed that this approach can improve GM-WM CNR in the presence of B1+ inhomogeneity, especially in some high relative B1+ areas where several key thalamic DBS targets are located. Additionally, in vivo images were acquired on a clinical 7T MRI to further validate this approach. Severe contrast degradation in the thalamus was observed when B1+ effect was not considered in sequence optimization, while the proposed approach yielded improved image contrast in the thalamus with key DBS targets well-defined. These results demonstrated that the proposed method allowed optimization of FGATIR on 7T to better visualize thalamic DBS targets, which may lead to improved DBS targeting accuracy as well as treatment outcome.

Protein engineering or candidate therapeutic peptide optimization are processes in which the identification of relevant sequence variants is critical. Starting from one amino-acid sequence, the choice of the substitutions must meet the objective of not disrupting the structure of the protein, not impacting the main functional properties of the starting entity, while also meeting the condition to enhance some expected property such as thermal stability, resistance to degradation, … Here, we introduce a new approach of sequence evolution that focuses on the objective of not disrupting the structure of the initial protein by embedding a point to point control on the preservation of the local structure at each position in the sequence. For 6 mini-proteins, we find that, starting from a single sequence, our simple approach intrinsically contains information about site-specific rate heterogeneity of substitution, and that it is able to reproduce sequence diversity as can be observed in the sequences available in the Uniref repository. We show that our approach is able to provide information about positions not to substitute and about substitutions not to perform at a given position to maintain structure integrity. Overall, our results demonstrate that point to point preservation of the local structure along a sequence is an important determinant of sequence evolution.

The irregular shapes and gravitational fields of asteroids challenge the design of collision-free asteroid surface hopping trajectories. A novel collision-free long-distance transfer trajectory design algorithm based on the convex optimization is presented in this article. A combinatorial keepout zone consisting of three spheres is defined to avoid the spacecraft colliding with the asteroid surface during the transfer. The boundary of the keepout zone is close to the surface of the target asteroid. With the Taylor expansion approximation, the nonconvex keepout zone constraint becomes convex. Two convex glide-slope constraints are employed for avoiding colliding during the ascent and descent. Thereafter, a convex optimization problem for solving a collision-free long-distance transfer trajectory between arbitrary two surface points is formulated. Moreover, an ant colony optimization algorithm combined with the proposed method is used to solve the optimal hopping sequence problem for exploring multiple points on the asteroid surface. Finally, several numerical simulation examples for the asteroid 433 Eros validate the feasibility and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The turbine rotor is the key component of the turbine, which has a great impact on the construction cost and power generation efficiency of an entire hydropower station. Receiving the torque of the runner transmission and completing the specified power generation is its main function. There are many uncertain factors in the design, manufacture, and operation environment of a turbine rotor. Therefore, it is necessary to optimize the mechanism on the premise of ensuring that the mechanical system meets high reliability and high safety levels. This article uses the multidisciplinary reliability analysis and optimization method under random and interval uncertainty to quantitatively analyze the uncertainty factors, and then optimally solves the RBMDO problem of the turbine rotor mechanism. Through the finite element simulation analysis of the optimized design scheme, the rationality and feasibility of the obtained results are further verified.

The protein design problem is to identify an amino acid sequence that folds to a desired structure. Given Anfinsen's thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the desired structure is the lowest energy state. As this calculation involves not only all possible amino acid sequences but also, all possible structures, most current approaches focus instead on the more tractable problem of finding the lowest-energy amino acid sequence for the desired structure, often checking by protein structure prediction in a second step that the desired structure is indeed the lowest-energy conformation for the designed sequence, and typically discarding a large fraction of designed sequences for which this is not the case. Here, we show that by backpropagating gradients through the transform-restrained Rosetta (trRosetta) structure prediction network from the desired structure to the input amino acid sequence, we can directly optimize over all possible amino acid sequences and all possible structures in a single calculation. We find that trRosetta calculations, which consider the full conformational landscape, can be more effective than Rosetta single-point energy estimations in predicting folding and stability of de novo designed proteins. We compare sequence design by conformational landscape optimization with the standard energy-based sequence design methodology in Rosetta and show that the former can result in energy landscapes with fewer alternative energy minima. We show further that more funneled energy landscapes can be designed by combining the strengths of the two approaches: the low-resolution trRosetta model serves to disfavor alternative states, and the high-resolution Rosetta model serves to create a deep energy minimum at the design target structure. [ABSTRACT FROM AUTHOR]

Abnormal metabolism γ-aminobutyric acid (GABA) is involved in many brain diseases, and in vivo GABA detection is important for the diagnosis of these diseases. This study optimized a commonly used spectral editing method for in vivo GABA detection, MEGA-PRESS. We used high-performance Shinnar-Le Roux (SLR) pulses to replace the commonly used Gaussian pulses. Single-band pulse and dual-band pulse were combined to excite the resonances of given metabolites. An optimized MEGA-PRESS sequence was implemented on the uMR780 magnetic resonance imaging system manufactured by United Imaging Healthcare (UIH). The results showed that the SLR pulse could reduce the interference of macromolecule (MM) on GABA signal. Compared with the traditional methods, combining the single-band and dual-band pulses could better suppress contamination of residual water signal to the GABA* signal. Volunteer experiments demonstrated that the optimized MEGA-PRESS sequence improved in vivo measurement of GABA* concentration.

Radiation therapy is among the top three cancer treatments in current medical services. The novel noncoplanar radiation therapy which claimed the best characteristics in almost all dosimetric properties encountered the challenges of the potential collision and the long time delivering. In this paper, we proposed a brand new scheme which uses a combined method of the collision avoidance path planning based on an improved probability roadmap method (PRM) and the delivery sequence optimization based on a modified genetic algorithm (GA) to solve the problems in noncoplanar radiation therapy. A uniform sampling strategy, an improved connection strategy, and an efficient local planner are introduced to optimize the roadmap result and accelerate the roadmap construction. The GA is improved by the elitist selection, the local search strategy, and the similar substitution strategy to achieve a better performance both in convergence rate and optimal solution. Experiments are carried out on the simulation platform with typical therapy system models. The results show that our proposed methods work well with the radiation therapy system in a compact working area. Collision is avoided and time consumption is reduced. We believe that our proposed algorithms could solve the problems in current radiation therapy and promote their clinic applications. [ABSTRACT FROM AUTHOR]

Abstract The finished geometry of a welded structure is greatly affected by heat-induced distortion from the welding process. This often requires correction by straightening and top-level machining to ensure manufacturing quality. Distortion is sensitive to weld sequencing and manufacturing environment. Optimal weld sequencing can significantly reduce distortion and manufacturing cost. However, an optimal weld sequence is not always intuitively obvious, especially for large structures with many welds. Virtual Fabrication Technology (VFT) is Caterpillar's proprietary software for the simulation of welding physics via finite element analysis. Powered by the highly successful VFT platform, a combinatorial genetic algorithm based optimization approach combined with an uncertainty quantification (UQ) module for evaluating robustness has been developed at Caterpillar for weld sequence optimization (WSO). WSO has been successfully applied to more than 50 structures, resulting in the minimization of straightening operations. The combined VFT and robust WSO approach results in an accelerated manufacturing process development schedule. We will present a summary of our WSO approach and some results that demonstrate how WSO has proven to be a transformative manufacturing planning technology at Caterpillar. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed strategy is aimed to solve the peak charging power demand issue of pure electric bus fast-charging station. It is effective for the charging power suppression of stations in the area with extremely limited distribution capacity. In order to reduce the recharging time of electric vehicles, the charging power and voltage are becoming higher, which has led to a huge distribution capacity demand and load fluctuation, especially in pure electric buses (PEBs) with large onboard batteries. Based on one actual direct current (DC) fast-charging station, a two-step strategy for the suppression of the peak charging power was developed in this paper, which combined charging optimization and a battery energy storage system (BESS) configuration. A novel charging strategy was proposed, with the PEBs fast-charging during operating hours and normal charging at night, based on a new charging topology. Then, a charging sequence optimization model was established, according to the operation characteristics analysis of the DC fast-charging station. The particle swarm optimization (PSO) algorithm is applied to optimize the charging sequence, which is disordered at present. Linear programming is used to configure the battery energy storage system in order to further decrease the peak charging power and satisfy the distribution capacity constraint. The two-step strategy was simulated by the dataset from the real station. The results show that the distribution capacity demand, charging load fluctuation, electricity cost, and size of the BESS were significantly decreased. [ABSTRACT FROM AUTHOR]

In order to reduce the recharging time of electric vehicles, the charging power and voltage are becoming higher, which has led to a huge distribution capacity demand and load fluctuation, especially in pure electric buses (PEBs) with large onboard batteries. Based on one actual direct current (DC) fast-charging station, a two-step strategy for the suppression of the peak charging power was developed in this paper, which combined charging optimization and a battery energy storage system (BESS) configuration. A novel charging strategy was proposed, with the PEBs fast-charging during operating hours and normal charging at night, based on a new charging topology. Then, a charging sequence optimization model was established, according to the operation characteristics analysis of the DC fast-charging station. The particle swarm optimization (PSO) algorithm is applied to optimize the charging sequence, which is disordered at present. Linear programming is used to configure the battery energy storage system in order to further decrease the peak charging power and satisfy the distribution capacity constraint. The two-step strategy was simulated by the dataset from the real station. The results show that the distribution capacity demand, charging load fluctuation, electricity cost, and size of the BESS were significantly decreased.

Microcystinase C (MlrC), one key hydrolase of the microcystinase family, plays an important role in linearized microsystin (L-MC) degradation. However, the three-dimensional structure and structural features of MlrC are still unclear. This study obtained high specific activity and high purity of MlrC by heterologous expression, and revealed that MlrC derived from Sphingomonas sp. ACM-3962 (ACM-MlrC) can degrade linearized products of MC-LR, MC-RR and MC-YR to product 3-amino-9-methoxy-2,6,8-trimethyl-10-phenyldeca-4,6-dienoic acid (Adda), indicating the degradation function and significance in MC-detoxification. More importantly, this study reported the crystal structure of ACM-MlrC at 2.6 Å resolution for the first time, which provides a basis for further understanding the structural characteristics and functions of MlrC. MlrC had a dual-domain feature, namely N and C terminal domain respectively. The N -terminal domain contained a Glutamate-Aspartate-Histidine-Histidine catalytic quadruplex coordinated with zinc ion in each monomer. The importance of zinc ions and their coordinated residues was analyzed by dialysis and site-directed mutagenesis methods. Moreover, the important influence of the N/C-terminal flexible regions of ACM-MlrC was also analyzed by sequence truncation, and then the higher yield and total activity of variants were obtained, which was beneficial to study the better function and application of MlrC. [Display omitted] • The first crystal structure of MlrC was solved at 2.6 Å resolution. • ACM-MlrC degraded linearized products of microcystin-LR/RR/YR into Adda. • The important influence of N/C terminal flexible regions for MlrC was analyzed. • The key role of zinc ion and its coordinated residues were studied in detail. • The higher yield, total activity and stability of ACM-MlrC variants were obtained. [ABSTRACT FROM AUTHOR]

Background: Codon usage plays a crucial role when recombinant proteins are expressed in different organisms. This is especially the case if the codon usage frequency of the organism of origin and the target host organism differ significantly, for example when a human gene is expressed in E. coli. Therefore, to enable or enhance efficient gene expression it is of great importance to identify rare codons in any given DNA sequence and subsequently mutate these to codons which are more frequently used in the expression host. Results: We describe an open-source web-based application, ATGme, which can in a first step identify rare and highly rare codons from most organisms, and secondly gives the user the possibility to optimize the sequence. Conclusions: This application provides a simple user-friendly interface utilizing three optimization strategies: 1. one-click optimization, 2. bulk optimization (by codon-type), 3. individualized custom (codon-by-codon) optimization. ATGme is an open-source application which is freely available at: http://atgme.org [ABSTRACT FROM AUTHOR]